Implementing and validating genomic selection in SRA breeding programs to accelerate improvements in yield, commercial cane sugar, and other key traits
Improvement in yields in sugarcane has been slow, particularly in the last decade, despite the fact that yield is one of the major drivers of profitability (with a 1% improvement in yield being worth $12.5M per annum to the Australian sugarcane industry).
The low rates of yield improvement reflect the fact that traits such as yield and sugar content are polygenic, which means that they are controlled by the combined effects of hundreds of genes. Conventional marker-assisted selection (such as that being developed in project 2018/005) works relatively poorly for polygenic traits, because any individual marker will only account for a small proportion of the variability for that trait.
This project is exploring a relatively new technology called genomic selection (GS), which is being widely applied to many genetically simpler species to accelerate genetic gains. GS differs from conventional marker-assisted selection because it uses advanced computing algorithms to simultaneously assess thousands of markers spread across the entire genome, and it therefore represents a much more powerful approach for polygenic traits.
In this project, GS is being applied for the first time in sugarcane, with a focus on improving polygenic traits such as yield and sugar content. A cost benefit analysis using computer simulation will determine the most profitable strategy for implementing genomic selection in the SRA breeding program. The project will conclude with a validation trial to demonstrate the effectiveness of genomic selection in SRA’s breeding program.
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